class: center, middle, inverse, title-slide .title[ # The Impact of Environmental Variability on Fishers’ Harvest Decisions in Chile ] .subtitle[ ## Using a Multi-Species Approach ] .author[ ### Felipe J. Quezada-Escalona ] .date[ ### Encuentro EfD Chile 2025 ] ---
# Introduction ## Big picture - Marine resource distribution and abundance is changing due to climate variability, with heterogenous spatial effects <a name=cite-Poloczanska2013-qq></a>([Poloczanska, Brown, Sydeman et al., 2013](#bib-Poloczanska2013-qq)). - Harvest levels would be affected <a name=cite-Quezada2023-hc></a>([Quezada, Tommasi, Frawley et al., 2023](#bib-Quezada2023-hc)), as well as price and value of catches, fishing costs, fishers’ incomes, among others <a name=cite-sumaila2011></a>([Sumaila, Cheung, Lam et al., 2011](#bib-sumaila2011)) ??? Recordar que uno apreta C para clonar, P para Presenter View, H para tener un mapa de las teclas --- # Introduction ## Research question How will fishing decisions, aggregate catch levels, and the price of marine resources be affected under different climatic scenarios in the multispecies small pelagic fishery (SPF) in Chile? - How do fishers **substitute between species**? - Contribute to the limited local literature on multi-species economic modeling in Chile - Understand fishers’ adaptive capacity helps to inform climate-resilient fisheries policies in Chile - See <a name=cite-Pena-Torres2017-gn></a>[Peña-Torres, Dresdner, and Vasquez (2017)](#bib-Pena-Torres2017-gn) for ENSO effects in Jack Mackerel fishery using discrete choice models. - We will focus in **climate variability** to estimate short-run responses. - i.e., climate change effect without adaptation <a name=cite-auffhammer2018></a>([Auffhammer, 2018](#bib-auffhammer2018)) --- # Introduction ## Why a Multi-Species Model? - Diversification is a good strategy: - Improves income stability and climate resilience <a name=cite-Kasperski2013-jz></a><a name=cite-Finkbeiner2015-bs></a>([Kasperski and Holland, 2013](#bib-Kasperski2013-jz); [Finkbeiner, 2015](#bib-Finkbeiner2015-bs)) - Fishers respond to environmental variability by: - Maintaining the current strategy - **Reallocating effort to other species/areas <a name=cite-Gonzalez-Mon2021-kj></a>([Gonzalez-Mon, Bodin, Lindkvist et al., 2021](#bib-Gonzalez-Mon2021-kj))** - Exiting the fishery <a name=cite-Powell2022-wj></a>([Powell, Levine, and Ordonez-Gauger, 2022](#bib-Powell2022-wj)) - Under multispecies harvesting is not straighforward to study fisher harvest decisions - Responses to availability vary by (i) port infrastructure, (ii) market access, and (iii) regulations ([Powell, Levine, and Ordonez-Gauger, 2022](#bib-Powell2022-wj)) - Different fishers, different choices <a name=cite-Jardine2020-um></a><a name=cite-Zhang2011-wv></a>([Jardine, Fisher, Moore et al., 2020](#bib-Jardine2020-um); [Zhang and Smith, 2011](#bib-Zhang2011-wv)) --- layout: false class: inverse, center, middle # Chile’s Small Pelagic Fishery --- # Chile’s Small Pelagic Fishery (SPF) ## Some facts - Mainly composed by anchoveta, Jack mackerel, Sardine - ~94% of national catch <a name=cite-SUBPESCA2020></a>([SUBPESCA, 2020](https://www.subpesca.cl/portal/618/articles-106845_documento.pdf)) - Mostly harvested with purse-seiners - In the Central-South (CS) region (Valparaiso-Los Lagos) all three species play a major role. - SPF have been used primarily for fishmeal and fish oil production [@Pena-Torres2017-gn] (~85% of jack mackerel for reduction) ## Regulations - Quota (TAC); divided between the small-scale and indutrial sector - Industrial sector operates under ITQ - RAE (*Regimen Artesanal de Extracción*) in some areas for small-scale fishery - Allocates regional quota to area or fishermen organization (i.e., catch shares) - Anchoveta and sardine are regulated as a mixed-species fishery --- # Chile’s Small Pelagic Fishery (SPF) ## Status of the stocks (CS) - Anchoveta: - Collapsed until 2018, - Overexploited in 2019, and has - Since 2020, within MSY limits. - Sardine: - Within MSY levels, except in 2021 and 2023 (overexploited) - Jack mackerel: - Overexploited until 2018, then within MSY limits. --- layout: false class: inverse, center, middle # Methods and data --- # Methodology Overview Based on <a name=cite-Kasperski2015-jm></a>[Kasperski (2015)](#bib-Kasperski2015-jm): 1. Econometrics models - Estimate stock dynamics - Estimate trip-level costs - Estimate annual trips - Estimate inverse demand 2. Simulations - Obtain optimal harvest and quota levels - Simulate climate change effects on profits, harvest and prices. --- # Data Sources - From IFOP: - Trip-level data - ID, departure and arrival times, vessel capacity, fleet and gear type, ports of departure and landing, haul timing and location, species, retained catch. - Annual stock abundance - Monthly landings by port. - Prices paid by processing plants (IFOP surveys; month-region) --- # Data Sources ## Environmental covariates From E.U. Copernicus Marine Service Information: - Global Ocean Physics Reanalysis <a name=cite-GLORYS12V1></a>([E.U. Copernicus Marine Service Information, 2025a](https://doi.org/10.48670/moi-00021)) - Daily salinity, sea surface temperature, and current speed and direction - Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model dataset <a name=cite-WIND_GLO_PHY></a>([E.U. Copernicus Marine Service Information, 2025b](https://doi.org/10.48670/moi-00185)) - Hourly wind speed and direction at the surface - Global Ocean Colour dataset <a name=cite-GlobColour></a>([E.U. Copernicus Marine Service Information, 2025c](https://doi.org/10.48670/moi-00281)) - Chlorophyll-a concentration **Some information:** - 2012–2024 - Chilean EEZ (32°S to 41°S) - Coarsest resolution: ~10 km --- # Data Sources ## Environmental covariates  --- # Data Sources ## To be requested - Average wage pay to crew member per hour (available?) - Diesel cost. - Permits by vessels - Quota prices (auction or secondary markets, if available) - Captures forward-looking behavior and information <a name=cite-Birkenbach2024></a>([Birkenbach, Lee, and Smith, 2024](https://doi.org/10.1086/727356)). - Simplify the dynamic model to a static one <a name=cite-reimer2022structural></a>([Reimer, Abbott, and Haynie, 2022](#bib-reimer2022structural)). - Quota by area/fishing organization for small-scale sector, and ITQ for industrial (by vessel?) - Information about Reallocations of quotas --- # Data Sources ## Data for projections Bio-ORACLE: - Only decadal (e.g., 2040–2050) projections - Different climate scenarios - SST, salinity, currents and chlorophyll (4km resolution) - No winds; CMIP6 for winds? (~100 km). --- # Data Sources ## Data for projections {width=10%} --- layout: false class: inverse, center, middle # Econometrics models --- # Model 1: Stock Dynamics `$$x_{i,y+1} + h_{iy} = \underbrace{(1 + r_i)x_{iy} + \eta_i x_{iy}^2}_{R_i(x_{iy})} + \underbrace{\sum_{j \neq i}^{n-1} a_{ij} x_{iy} x_{jy}}_{I_i(x_y)} + \rho_i Env_{iy} + \varepsilon_{iy} \quad i=1,\ldots,n$$` - where: - `\(x_{iy}\)` is the fish stock by species `\(i=1,\ldots,n\)` in year `\(y\)`, `\(n\)` is the total number of species, - `\(h_{iy}\)` is the annual harvest of species `\(i\)` on year `\(y\)`, - `\(r_i\)` is the intrinsic growth rate of the resource `\(i\)`, - `\(\eta_i\)` is a density-dependent factor related to the carrying capacity, - `\(\alpha_{ij}\)` are the interaction parameters between species. - `\(Env_{iy}\)` includes environmental covariates (SST and chlorophyll) The system of `\(n\)` growth equations can be estimated simultaneously using SUR --- # Model 2: Trip-Level Costs `$$C_{vg} = \sum_{i=1}^{2n+M+k} \alpha_{g, \mathbf{X}_i} \mathbf{X}_{ivg} + \frac{1}{2} \sum_{i=1}^{2n+M+k} \sum_{j=1}^{2n+M+k} \alpha_{g, \mathbf{X}_i\mathbf{X}_j} \mathbf{X}_{ivg} \mathbf{X}_{jvg}$$` where: - `\(C_{vg}= w z_{vg}^*\)` is the total cost incurred by vessel `\(v=1,\ldots,V_g\)` conditional on gear used `\(g=1,\ldots,G\)`: - `\(z_{vg}^*\)` is the optimal quantity of input used, (e.g., crew members, time spent at sea, distance traveled?) - `\(w\)` is a matrix of variable input prices, - `\(\mathbf{X}^{'}_{vg} = [w;h_{vg};x;Z_v;Env]\)` is a matrix of explanatory variables, and `\(\mathbf{X}_{ivg}\)` represents the *i*th column of the `\(\mathbf{X}_{vg}\)`: - `\(h_{vg}\)` is a matrix of harvest quantities, - `\(x\)` is a matrix of given stock levels of the species of interest, and - `\(Z_v\)` is a matrix of given vessel characteristics. - `\(Env\)` is a matrix of **environmental covariates** - e.g., wind intensity and wave conditions in each trip at the harvest location? (or within a port radius?) --- # Model 3: Total Annual Trips The number of trips a vessel will take in a given year for each gear type used is assumed to follow a Poison distribution: `$$Pr\left[T^{*}_{vgy} = t_v\right] = \frac{exp^{-exp(U^{'}_{vg}\beta_g)}exp(U^{'}_{vg}\beta_g)^{t_v}}{t_v !}$$` where - `\(U_{vg}=[p,w,h_{vg},\bar{q},Z_{vg}, Env]\)` is a matrix of explanatory variables, - `\(p\)` is a matrix of species prices, - `\(w\)` is a matrix of variable input prices, - `\(h_{vg}\)` is a matrix of harvest quantities, - `\(\bar{q}\)` is the annual quota level. - `\(Z_v\)` is a matrix of given vessel characteristics. - `\(Env\)` include variables that reflect annual weather conditions - Accumulation of *bad weather days*? or *number of storms*? - Other variables? State dependency? - `\(\beta_g\)` is a vector of coefficients to be estimated, - `\(t_v\)` is the number of trips taken by vessel `\(v\)` using gear type `\(g\)` in year `\(y\)`, and --- # Model 4: Inverse Demand The price of each species is modeled using an Inverse Almost Ideal Demand System (IADS). The log of the price `\(p_{iy}\)` of a species `\(i\)` in year `\(y\)` is the following: `$$\ln p_{iy} = \sum_j^n \gamma_j \ln h_{j,y} + \gamma_{H} \ln H_y + \gamma_{FM} \ln P^\text{FishMeal}_y + \epsilon_{iy}$$` where: - `\(H_y = \sum h_{j,y}\)` - `\(P^\text{FishMeal}_y\)` is the fish meal world price Harvest may be endogenous - Three Stage Least Squares (3SLS) procedure, - `\(h_{j,y}\)` instrumented by variables that affect supply function such as *SST*, *Chl*, and fuel prices. --- layout: false class: inverse, center, middle # Numerical optimization --- # Integration and Simulation Use models parameters to: - Obtain the optimal **harvest** and **quota** using historical data - Conduct numerical optimization to obtain optimal **harvest** and **quota** conditional on climate scenario. - Evaluate **profits** and **species substitution** --- # Numerical optimization ## Vessel maximization problem In each year, a vessel maximizes profits by choosing their optimal number of trips `\(T_g\)` and harvest levels per trip `\(h_{g\tau}\)` given a gear type: `$$\begin{align*} \max_{h_{gt}, T_g} \quad \pi_{vgt} & = \sum_{\tau=t}^{T_g} \rho^\tau \left\{ P(h) h_{g\tau} - C_g(h_{g\tau} | w, x, Z, Env) \right\} \quad \tau = t,\ldots, T_g \nonumber \\ \textbf{s.t} \quad q_{g,t+1} & = \omega \ast \bar{q} - \sum_{t=1}^{t} h_{gt} \geq 0, \quad t = 1, \dots, T-1, \quad g = 1, \dots, G \end{align*}$$` - where: - `\(\rho\)` is the intra-annual discount factor, - `\(\omega\)` is a vector of shares of `\(\bar{q}\)`, and - `\(h_{lt}=0\)` for all `\(l\neq g\)`. --- # Numerical optimization ## Some considerations - The vector of shares is obtained from historical data on harvest. - The optimal profit from the maximization problem is `\(\pi_{vgy}^* (p,w,x,Z,\bar{q},\omega, Env)\)`, - `\(h_{vgty}^*\)` is the optimal harvest per trip. - `\(T_{vgy}^*\)` optimal total number of trips. - Optimal quota level, per year and by species, is obtained by solving the social-planner optimization problem to maximize the net value of the fishery --- layout: false class: inverse, center, middle # Preliminary results --- # Is there any subtitution? ## Small-scale vessels <img src="data:image/png;base64,#figs/strategy_transitions_ART.svg" width="95%" style="display: block; margin: auto;" /> --- # Is there any subtitution? ## Industrial vessels <img src="data:image/png;base64,#figs/strategy_transitions_IND.svg" width="95%" style="display: block; margin: auto;" /> --- # Stock dynamics <img src="data:image/png;base64,#tables/table_SUR_stock.png" width="80%" style="display: block; margin: auto;" /> --- # Stock dynamics - SST and CHL improves model performance (F = 1.908; `\(p\)`-value = 0.07). - Test if I can exclude interactions terms - Herrick, et al. (2009): "Sardine is known to be more productive during warm-water regimes in the California Current ecosystem." - Need to calculate robust standard error (by hand?) --- # What is next? - Finish biomass estimations - Start soon with total annual trips - Hopefully a short paper can came up from that work - Two undergrad students working on the inverse demand (i.e. price) module for their thesis... - Results by July 2026 - If they have time, they will also analyze long-run and short-run dynamics with a VEC - Write paper from their dissertation --- layout: false class: inverse, center, middle # ¡Muchas gracias! <span style="color:#f59f18; font-size:1.3em; font-weight:bold;">¿Preguntas?</span> <div style="margin-top: 50px;"></div> **Felipe J. Quezada-Escalona** <img src="https://fquezadae.github.io/Slides-Econometria/figs/depto_economia_blanco.png" width="250"> <a href="https://felipequezada.com" target="_blank" style=" font-size:1em; background:linear-gradient(#f59f18); -webkit-background-clip:text; -webkit-text-fill-color:transparent; text-decoration:none; "> 🌐 felipequezada.com </a> <!-- --- --> <!-- # Temas de investigación en curso --> <!-- - Modelos de elección discreta para el estudio del efecto de cambios en la distribución de especies pelágicas en las decisiones de pesca en la costa oeste de EEUU (localización, especie y participación) -- Enviado a *Ecological Economics* --> <!-- - ¿Hacer lo mismo para Chile? Idea: basados en [Birkenbach, Lee, and Smith (2024)](https://doi.org/10.1086/727356), crear contrafactuales para fraccionamiento, o cambios en variables climáticas. Validar con periodos observados. --> <!-- - ¿Como endogeneizar los precios en modelos de eleccion discreta, los cuales dependen de la frecuencia en que pescadores participan? (ojo: no es lo mismo que instrumentar) --> <!-- - Colaborando con NOAA Fisheries en: --> <!-- - El desarrollo de proyecciones de desembarque futuro bajo distintos modelos climáticos que afectan la distribución de especies. --> <!-- - La incorporación de un modelo de teoría de juegos a modelos de evaluación de stock que consideran especies transfronterizas. --> <!-- --- --> <!-- # Areas de posible colaboración con INCAR2/SE --> <!-- - Impacto de la variación climática (u otro shock exógeno?) en las decisiones de cosecha de centros de cultivos (o productores de pequeña escala)? --> <!-- - Algo similar a lo hecho con SPF para USA --> <!-- - Es como lo que hace Adams con HAB pero tal vez con modelo de elección discreta en vez de modelo de causalidad -- Crear contrafactuales como en [Birkenbach, Lee, and Smith (2024)](https://doi.org/10.1086/727356). --> <!-- - Relación entre las etapas de producción en áreas estuarinas y marinas (ejemplo: ¿La eficiencia técnica en agua dulce predice eficiencia técnica en etapa de agua salada?). --> <!-- - Estimar patrones de sustitución de demanda de salmón en *XX* país usando modelo BLP, y como estos patrones se pudieron ver afectados por algún evento (e.g., marea roja) -- Idea de Manuel --> <!-- --- --> <!-- # Areas de posible colaboración con INCAR2/SE --> <!-- - ¿Conectar el modelo para SPF ha elaborar en el proyecto FONDECYT a los costos de alimentación de la acuicultura en Chile? (Comentario de Jorge cuando me adjudique el FONDECYT) --> <!-- - Early Warning System usando indicadores económicos (Estoy inscrito en ese tema en la línea de Renato -- Adams Ceballos le interesa participar) --> <!-- --- --> --- # References <a name=bib-auffhammer2018></a>[Auffhammer, M.](#cite-auffhammer2018) (2018). "Quantifying economic damages from climate change". In: _Journal of Economic Perspectives_ 32.4, pp. 33-52. <a name=bib-Birkenbach2024></a>[Birkenbach, A. M., M. Lee, and M. D. Smith](#cite-Birkenbach2024) (2024). "Counterfactual Modeling of Multispecies Fisheries Outcomes under Market-Based Regulation". In: _Journal of the Association of Environmental and Resource Economists_ 11.3, pp. 755-796. DOI: [10.1086/727356](https://doi.org/10.1086%2F727356). eprint: https://doi.org/10.1086/727356. <a name=bib-GlobColour></a>[E.U. Copernicus Marine Service Information](#cite-GlobColour) (2025c). _Global Ocean Colour (Copernicus-GlobColour)_. Accessed on 23-SEP-2025. DOI: [10.48670/moi-00281](https://doi.org/10.48670%2Fmoi-00281). <a name=bib-WIND_GLO_PHY></a>[E.U. Copernicus Marine Service Information](#cite-WIND_GLO_PHY) (2025b). _Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model_. Accessed on 23-SEP-2025. DOI: [10.48670/moi-00185](https://doi.org/10.48670%2Fmoi-00185). <a name=bib-GLORYS12V1></a>[E.U. Copernicus Marine Service Information](#cite-GLORYS12V1) (2025a). _Global Ocean Physics Reanalysis_. Accessed on 23-SEP-2025. DOI: [10.48670/moi-00021](https://doi.org/10.48670%2Fmoi-00021). --- # References <a name=bib-Finkbeiner2015-bs></a>[Finkbeiner, E. M.](#cite-Finkbeiner2015-bs) (2015). "The role of diversification in dynamic small-scale fisheries: Lessons from Baja California Sur, Mexico". In: _Glob. Environ. Change_ 32, pp. 139-152. <a name=bib-Gonzalez-Mon2021-kj></a>[Gonzalez-Mon, B., Ö. Bodin, E. Lindkvist, et al.](#cite-Gonzalez-Mon2021-kj) (2021). "Spatial diversification as a mechanism to adapt to environmental changes in small-scale fisheries". In: _Environ. Sci. Policy_ 116, pp. 246-257. <a name=bib-Jardine2020-um></a>[Jardine, S. L., M. C. Fisher, S. K. Moore, et al.](#cite-Jardine2020-um) (2020). "Inequality in the Economic Impacts from Climate Shocks in Fisheries: The Case of Harmful Algal Blooms". In: _Ecol. Econ._ 176, p. 106691. <a name=bib-Kasperski2015-jm></a>[Kasperski, S.](#cite-Kasperski2015-jm) (2015). "Optimal Multi-species Harvesting in Ecologically and Economically Interdependent Fisheries". In: _Environ. Resour. Econ._ 61.4, pp. 517-557. <a name=bib-Kasperski2013-jz></a>[Kasperski, S. and D. S. Holland](#cite-Kasperski2013-jz) (2013). "Income diversification and risk for fishermen". En. In: _Proc. Natl. Acad. Sci. U. S. A._ 110.6, pp. 2076-2081. --- # References <a name=bib-Pena-Torres2017-gn></a>[Peña-Torres, J., J. Dresdner, and F. Vasquez](#cite-Pena-Torres2017-gn) (2017). "El Niño and Fishing Location Decisions: The Chilean Straddling Jack Mackerel Fishery". In: _Mar. Resour. Econ._ 32.3, pp. 249-275. <a name=bib-Poloczanska2013-qq></a>[Poloczanska, E. S., C. J. Brown, W. J. Sydeman, et al.](#cite-Poloczanska2013-qq) (2013). "Global imprint of climate change on marine life". En. In: _Nat. Clim. Chang._ 3.10, pp. 919-925. <a name=bib-Powell2022-wj></a>[Powell, F., A. Levine, and L. Ordonez-Gauger](#cite-Powell2022-wj) (2022). "Climate adaptation in the market squid fishery: fishermen responses to past variability associated with El Niño Southern Oscillation cycles inform our understanding of adaptive capacity in the face of future climate change". En. In: _Clim. Change_ 173.1-2, p. 1. <a name=bib-Quezada2023-hc></a>[Quezada, F. J., D. Tommasi, T. H. Frawley, et al.](#cite-Quezada2023-hc) (2023). "Catch as catch can: markets, availability, and fishery closures drive distinct responses among the U.S. West Coast coastal pelagic species fleet segments". En. In: _Can. J. Fish. Aquat. Sci._. Just-IN. <a name=bib-reimer2022structural></a>[Reimer, M. N., J. K. Abbott, and A. C. Haynie](#cite-reimer2022structural) (2022). "Structural behavioral models for rights-based fisheries". In: _Resource and Energy Economics_ 68, p. 101294. --- # References <a name=bib-SUBPESCA2020></a>[SUBPESCA](#cite-SUBPESCA2020) (2020). _Informe Sectorial de Pesca y Acuicultura 2019_. Accessed: 02-04-2025. URL: [https://www.subpesca.cl/portal/618/articles-106845_documento.pdf](https://www.subpesca.cl/portal/618/articles-106845_documento.pdf). <a name=bib-sumaila2011></a>[Sumaila, U. R., W. W. Cheung, V. W. Lam, et al.](#cite-sumaila2011) (2011). "Climate change impacts on the biophysics and economics of world fisheries". In: _Nature climate change_ 1.9, pp. 449-456. <a name=bib-Zhang2011-wv></a>[Zhang, J. and M. D. Smith](#cite-Zhang2011-wv) (2011). "Heterogeneous Response to Marine Reserve Formation: A Sorting Model approach". In: _Environ. Resour. Econ._ 49.3, pp. 311-325.